BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning
in Sentiment Analysis
- URL: http://arxiv.org/abs/2203.11702v1
- Date: Tue, 22 Mar 2022 13:12:27 GMT
- Title: BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning
in Sentiment Analysis
- Authors: Ahmed Murtadha, Shengfeng Pan, Bo Wen, Jianlin Su, Wenze Zhang,
Yunfeng Liu
- Abstract summary: We propose to address aspect categorization and aspect-based sentiment subtasks in a unified framework.
We first introduce a mechanism that collaborates the semantic and syntactic information to construct auxiliary-sentences for the implicit aspect.
We then encourage BERT to learn the aspect-specific representation in response to the automatically constructed auxiliary-sentence.
- Score: 4.008465268899542
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Aspect-based sentiment analysis (ABSA) task aims to associate a piece of text
with a set of aspects and meanwhile infer their respective sentimental
polarities. Up to now, the state-of-the-art approaches are built upon
fine-tuning of various pre-trained language models. They commonly aim to learn
the aspect-specific representation in the corpus. Unfortunately, the aspect is
often expressed implicitly through a set of representatives and thus renders
implicit mapping process unattainable unless sufficient labeled examples.
In this paper, we propose to jointly address aspect categorization and
aspect-based sentiment subtasks in a unified framework. Specifically, we first
introduce a simple but effective mechanism that collaborates the semantic and
syntactic information to construct auxiliary-sentences for the implicit aspect.
Then, we encourage BERT to learn the aspect-specific representation in response
to the automatically constructed auxiliary-sentence instead of the aspect
itself. Finally, we empirically evaluate the performance of the proposed
solution by a comparative study on real benchmark datasets for both ABSA and
Targeted-ABSA tasks. Our extensive experiments show that it consistently
achieves state-of-the-art performance in terms of aspect categorization and
aspect-based sentiment across all datasets and the improvement margins are
considerable.
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